1. Field of the Invention
The invention generally relates to use of facial recognition technology as used in surveillance and access systems and is specifically directed to incorporation of such technology in an IP compatible, networked, comprehensive multimedia surveillance system.
2. Discussion of the Prior Art
My earlier patents and applications have covered various aspects of the networked multimedia surveillance system in detail. My following earlier patents and pending applications are incorporated herein by reference:
Serial #10/192,870Filing Date: Jul. 10, 2002Title: Comprehensive Multi-Media Surveillance andResponse System for Aircraft, Operations Centers, Airportsand Other Commercial Transports, Centers and TerminalsSerial #08/738,487Filing Date: Oct. 28, 1996Patent #5,798,458Issue Date: Aug. 25, 1998Title: Acoustic Catastrophic Event Detection andData Capture and Retrieval System for AircraftSerial #08/745,536Filing Date: Nov. 12, 1996Patent #6,009,356Issue Date: Dec. 28, 1999Title: Wireless Transducer Data Capture andRetrieval System for AircraftSerial #08/815,026Filing Date: Mar. 14, 1997Patent #5,943,140Issue Date: Aug. 24, 1999Title: Method and Apparatus for Sending andReceiving Facsimile Transmissions Over a Non-TelephonicTransmission SystemSerial #09/143,232Filing Date: Aug. 28, 1998Title: Multifunctional Remote Control Systemfor Audio Recording, Capture, Transmission andPlayback of Full Motion and Still ImagesSerial #09/257,448Filing Date: Feb. 25, 1999Title: Multi-Casting CommunicationProtocols for Simultaneous Transmission toMultiple StationsSerial #09/257,720Filing Date: Feb. 25, 1999Patent #6,392,692Issue Date: May 21, 2002Title: Network Communication Techniquesfor Security Surveillance and Safety SystemSerial #09/257,765Filing Date: Feb. 25, 1999Patent #6,366,311Issue Date: Apr. 02, 2002Title: Record and Playback System for AircraftSerial #09/257,767Filing Date: Feb. 25, 1999Patent #6,246,320Issue Date: Jun. 12, 2001Title: Ground Link With On-Board SecuritySurveillance System for Aircraft and OtherCommercial VehiclesSerial #09/257/769Filing Date: Feb. 25, 1999Title: Ground Based Security Surveillance Systemfor Aircraft and Other Commercial VehiclesSerial #09/257,802Filing Date: Feb. 25, 1999Patent #6,253,064Issue Date: Jun. 26, 2001Title: Terminal Based Traffic Management andSecurity Surveillance System for Aircraft and OtherCommercial VehiclesSerial #09/593,901Filing Date: Jun. 14, 2000Title: Dual Mode CameraSerial #09/594,041Filing Date: Jun. 14, 2000Title: Multimedia Surveillance and MonitoringSystem Including Network ConfigurationSerial #09/687,713Filing Date: Oct. 13, 2000Title: Apparatus and Method of Collectingand Distributing Event Data to StrategicSecurity Personnel and Response VehiclesSerial #09/966,130Filing Date: Sep. 21, 2001Title: Multimedia Network Appliancesfor Security and Surveillance ApplicationsSerial #09/974,337Filing Date: Oct. 10, 2001Title: Networked Personal Security SystemSerial #09/715,783Filing Date: Nov. 17, 2000Title: Multiple Video Display Configurationsand Bandwidth Conservation Scheme forTransmitting Video Over a NetworkSerial #09/716,141Filing Date: Nov. 17, 2000Title: Method and Apparatus forDistributing Digitized Streaming Video Over aNetworkSerial #09/725,368Filing Date: Nov. 29, 2000Title: Multiple Video Display Configurationsand Remote Control of Multiple Video SignalsTransmitted to a Monitoring Station Over a NetworkSerial #09/853,274Filing Date: May 11, 2001Title: Method and Apparatus for Collecting,Sending, Archiving and Retrieving Motion Video andStill Images and Notification of Detected EventsSerial #09/854,033Filing Date: May 11, 2001Title: Portable, Wireless Monitoring andControl Station for Use in Connection With aMulti-Media Surveillance System Having EnhancedNotification FunctionsSerial #09/866,984Filing Date: May 29, 2001Title: Modular Sensor ArraySerial #09/960,126Filing Date: Sep. 21, 2001Title: Method and Apparatus for InterconnectivityBetween Legacy Security Systems and Networked MultimediaSecurity Surveillance SystemSerial #10/134,413Filing Date: Apr. 29, 2002Title: Method for Accessing and Controlling aRemote Camera in a Networked System With MultipleUser Support Capability and Integration to OtherSensor Systems
Several companies have developed computer algorithms that are capable of producing a “digital signature” from video images of people's faces. These signatures are much like a fingerprint: they are unique to individuals; they are relatively small so they are efficient; and, they may be used in databases to look up the identity and other data about the person.
While other types of biometrics, such as iris scanning, are at best or even more accurate than facial recognition (which has a relatively low error rate; just under 1 percent), facial recognition will probably be accepted more widely because it is not intrusive. Further, it does not require that the user push, insert or click on anything. Companies often do not need to install anything beyond the new software because most already have cameras in place and pictures of employees on file—making it less expensive than iris reading setups. In addition, the relatively small size of the database for a facial profile makes it an attractive technology.
One example of a currently available facial recognition software is the Visionics' FaceIt system. The FaceIt software measures a face according to its peaks and valleys—such as the tip of the nose, the depth of the eye sockets—which are known as nodal points. A typical human face has 80 nodal points and precise recognition can be achieved with as few as 14 to 22 utilizing the FaceIt system. Specifically, the FaceIt system concentrates on the inner region of the face, which runs from temple to temple and just over the lip, called the ‘golden triangle.’ This is the most stable because even if facial hair such as a beard is altered, or if the subject changes or adds glasses, changes in weight or ages substantially the ‘golden triangle’ region tends to not be affected, while places such as under chin would be substantially altered. FaceIt plots the relative positions of these points and comes up with a long string of numbers, called a faceprint.
Visage Technology of Littleton, Mass., has a slightly different model. Its software compares faces to 128 archetypes it has on record. Faces are then assigned numbers according to how they are similar or different from these models. The Visage Technology has been utilized to date in the identification of criminals, for access control, for transaction security and for identity fraud prevention.
Most recently, government and aviation officials are poised to begin using facial recognition systems to scan airport terminals for suspected terrorists. Recently, Visionics has teamed up with a domestic airline to demonstrate a conceptual boarding system that will use FaceIt to facilitate the rapid boarding of the airline's frequent flyers.
In the past, law enforcement officials often have no more than a facial image to link a suspect to a particular crime or previous event. Up to now, database searches were limited to textual entries (i.e., name, social security number, birth date, etc.), leaving room for error and oversight. By conducting searches against facial images, the facial recognition technology permits rapid review of information and quickly generated results, with the ability to check literally millions of records for possible matches, and then automatically and reliably verifying the identity of a suspect.
The facial recognition technology has several advantages over other biometric systems. For example, with facial recognition technology a person can be identified at a distance or in a crowd. The technology has the capability of capturing a face in the field of view, extract the face from the background data and compare it against a database.
The system permits the creation of watch lists or the like. This could include, for example, known shoplifters, terrorists or criminals, as well as frequent customers, VIP's, expected visitors or individuals generally classified as friends or foes. The system can be used at airports, casinos, public buildings, schools, subways, colleges, factories, business facilities, housing complexes, residences and the like.
The system also is useful in transaction modes. Customers are used to being verified or being recognized by their face at retail locations by providing merchants with a driver's license or other form of photo ID. In sharp contrast to today's widely used signature verification process, which is highly unreliable and cannot be accurately determined by unskilled and untrained clerks, face recognition makes verification reliable, automatic and fast. In banking, facial recognition technology can adapt to already installed ATM cameras for recognizing and verifying customer identities so the financial transaction can be quickly and effortlessly conducted. Such technology can replace reliance on alphanumeric PINs to identify and authenticate a user.
Face recognition is the only biometric that can be used in two modalities—logon and continuous monitoring. An example of logon modality is use as a perimeter defense mechanism, where an authorized individual gains entry to a network or session after a one-time logon process. This is the typical mode for all biometric systems. In addition, face recognition supports a continuous monitoring mode where persons are continuously authenticated for ensuring that at all times the individual in front of the computer or handheld device continues to be the same authorized person who logged in.
Currently available technology focuses on the following aspects of facial recognition:
Detection—When the system is attached to a video surveillance system, the recognition software searches the field of view of a video camera for faces. If there is a face in the view, it is detected within a fraction of a second. A multi-scale algorithm is used to search for faces in low resolution. The system switches to a high-resolution search only after a head-like shape is detected.
Alignment—Once a face is detected, the system determines the head's position, size and pose to assure that the face is appropriately turned toward the camera for the system to register it.
Normalization—The image of the head is scaled and rotated so that it can be registered and mapped into an appropriate size and pose. Normalization is performed regardless of the head's location and distance from the camera.
Representation—The system translates the facial data into a unique code. This coding process allows for easier comparison of the newly acquired facial data to stored facial data.
Matching—The newly acquired facial data is compared to the stored data and linked to at least one stored facial representation.
The heart of current facial recognition systems is the algorithm. This is the mathematical technique the system uses to encode faces. The system maps the face and creates a faceprint, a unique numerical code for that face. Once the system has stored a faceprint, it can compare it to the thousands or millions of faceprints stored in a database. In the FaceIt system, each faceprint requires an 84-byte file. The FaceIt system can match multiple faceprints at a rate of up to 60 million per minute. As comparisons are made, the system assigns a value to the comparison using a scale of 1 to 10. If a score is above a predetermined threshold, a match is declared. The operator then views the two photos that have been declared a match to be certain that the computer is accurate.
As the facial recognition technology develops, expanding uses are desirable. A comprehensive, system approach incorporating this technology with other legacy, digital and IP system architectures is needed. A comprehensive, coordinated approach utilizing this technology with known surveillance techniques and with system collection, distribution and management techniques will be required to maximize the value of this and other biometric recognition technologies.